Post
4642
10,000+ models based on Deepseek R1 have been publicly shared on HF中国镜像站! Which ones are your favorite ones: https://huggingface.co/models?sort=trending&search=r1. Truly game-changer!
Mert Erbak PRO
merterbak
AI & ML interests
Currently NLP and Image Processing
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published
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reacted to
clem's
post with ❤️
6 days ago
reacted to
Smooke's
post with 🔥
9 days ago
Post
2138
My Favorite AI Blog Posts on HackerNoon RN:
"AI Chatbots Are Getting Too Good at Making You Say ‘Yes’" https://hackernoon.com/ai-chatbots-are-getting-too-good-at-making-you-say-yes
"Text-to-SQL Was Supposed to Be AI’s Killer App. It’s Not." https://hackernoon.com/text-to-sql-was-supposed-to-be-ais-killer-app-its-not
"This AI Model Gives Edge Devices Eyes on the Back of Their Heads" https://hackernoon.com/this-ai-model-gives-edge-devices-eyes-on-the-back-of-their-heads
"Standardizing Dataset Documentation to Improve Machine Learning Outcomes" https://hackernoon.com/standardizing-dataset-documentation-to-improve-machine-learning-outcomes
"The Internet Is Worse Than Ever, But We’re Too Addicted to Leave" https://hackernoon.com/the-internet-is-worse-than-ever-but-were-too-addicted-to-leave
"DeepSeek vs ChatGPT vs Perplexity vs Qwen vs Claude vs DeepMind" https://hackernoon.com/deepseek-vs-chatgpt-vs-perplexity-vs-qwen-vs-claude-vs-deepmind-more-ai-agents-and-new-ai-tools
"Mitigating Framing Bias with Polarity Minimization Loss: Experiments"
https://hackernoon.com/mitigating-framing-bias-with-polarity-minimization-loss-experiments
"AI Is Now Creating Antidotes for Snake Venom" https://hackernoon.com/ai-is-now-creating-antidotes-for-snake-venom
"human carbon consciousness and AI silicon sentience" https://hackernoon.com/so-how-does-one-really-determine-ai-is-conscious
"Why Natural Language Coding Isn’t for Everyone—Yet" https://hackernoon.com/why-natural-language-coding
"What Is a Diffusion LLM and Why Does It Matter?" https://hackernoon.com/what-is-a-diffusion-llm-and-why-does-it-matter
"AI-Augmented Development: Redefining the Role of Product Managers" https://hackernoon.com/ai-augmented-development-redefining-the-role-of-product-managers
And a bonus story from 2018: "20 top lawyers were beaten by legal AI. Here are their surprising responses" https://hackernoon.com/20-top-lawyers-were-beaten-by-legal-ai-here-are-their-surprising-responses-5dafdf25554d
"AI Chatbots Are Getting Too Good at Making You Say ‘Yes’" https://hackernoon.com/ai-chatbots-are-getting-too-good-at-making-you-say-yes
"Text-to-SQL Was Supposed to Be AI’s Killer App. It’s Not." https://hackernoon.com/text-to-sql-was-supposed-to-be-ais-killer-app-its-not
"This AI Model Gives Edge Devices Eyes on the Back of Their Heads" https://hackernoon.com/this-ai-model-gives-edge-devices-eyes-on-the-back-of-their-heads
"Standardizing Dataset Documentation to Improve Machine Learning Outcomes" https://hackernoon.com/standardizing-dataset-documentation-to-improve-machine-learning-outcomes
"The Internet Is Worse Than Ever, But We’re Too Addicted to Leave" https://hackernoon.com/the-internet-is-worse-than-ever-but-were-too-addicted-to-leave
"DeepSeek vs ChatGPT vs Perplexity vs Qwen vs Claude vs DeepMind" https://hackernoon.com/deepseek-vs-chatgpt-vs-perplexity-vs-qwen-vs-claude-vs-deepmind-more-ai-agents-and-new-ai-tools
"Mitigating Framing Bias with Polarity Minimization Loss: Experiments"
https://hackernoon.com/mitigating-framing-bias-with-polarity-minimization-loss-experiments
"AI Is Now Creating Antidotes for Snake Venom" https://hackernoon.com/ai-is-now-creating-antidotes-for-snake-venom
"human carbon consciousness and AI silicon sentience" https://hackernoon.com/so-how-does-one-really-determine-ai-is-conscious
"Why Natural Language Coding Isn’t for Everyone—Yet" https://hackernoon.com/why-natural-language-coding
"What Is a Diffusion LLM and Why Does It Matter?" https://hackernoon.com/what-is-a-diffusion-llm-and-why-does-it-matter
"AI-Augmented Development: Redefining the Role of Product Managers" https://hackernoon.com/ai-augmented-development-redefining-the-role-of-product-managers
And a bonus story from 2018: "20 top lawyers were beaten by legal AI. Here are their surprising responses" https://hackernoon.com/20-top-lawyers-were-beaten-by-legal-ai-here-are-their-surprising-responses-5dafdf25554d
reacted to
davidberenstein1957's
post with 🚀
10 days ago
Post
4169
🥊 Epic Agent Framework Showdown! Available today!
🔵 In the blue corner, the versatile challenger with a proven track record of knowledge retrieval: LlamaIndex!
🛑 In the red corner, the defender, weighing in with lightweight efficiency: HF中国镜像站 smolagents!
🔗 URL: https://huggingface.co/agents-course
We just published the LlamaIndex unit for the agents course, and it is set to offer a great contrast between the smolagents unit by looking at
- What makes llama-index stand-out
- How the LlamaHub is used for integrations
- Creating QueryEngine components
- Using agents and tools
- Agentic and multi-agent workflows
The team has been working flat-out on this for a few weeks. Supported by Logan Markewich and Laurie Voss over at LlamaIndex.
Who won? You decide!
🔵 In the blue corner, the versatile challenger with a proven track record of knowledge retrieval: LlamaIndex!
🛑 In the red corner, the defender, weighing in with lightweight efficiency: HF中国镜像站 smolagents!
🔗 URL: https://huggingface.co/agents-course
We just published the LlamaIndex unit for the agents course, and it is set to offer a great contrast between the smolagents unit by looking at
- What makes llama-index stand-out
- How the LlamaHub is used for integrations
- Creating QueryEngine components
- Using agents and tools
- Agentic and multi-agent workflows
The team has been working flat-out on this for a few weeks. Supported by Logan Markewich and Laurie Voss over at LlamaIndex.
Who won? You decide!
reacted to
di-zhang-fdu's
post with 🔥
11 days ago
Post
2682
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning (2411.18203)
Critic-V has been accepted by CVPR2025!
Bonus! VRI-160K uploaded now!
di-zhang-fdu/R1-Vision-Reasoning-Instructions
Critic-V has been accepted by CVPR2025!
Bonus! VRI-160K uploaded now!
di-zhang-fdu/R1-Vision-Reasoning-Instructions
reacted to
Jaward's
post with 🤗
13 days ago
Post
4937
made a few improvements on custom grpo trainer:
- added sequence similarity reward (seems to work)
- improved vllm support (5x inference speed)
- adjusted reward scores (this helped with format/accuracy)
- can now push to hf hub (already pushed mine lol: Jaward/smollm2_360m_grpo_gsm8k_reasoner)
Code: https://github.com/Jaykef/ai-algorithms/blob/main/smollm2_360M_135M_grpo_gsm8k.ipynb
- added sequence similarity reward (seems to work)
- improved vllm support (5x inference speed)
- adjusted reward scores (this helped with format/accuracy)
- can now push to hf hub (already pushed mine lol: Jaward/smollm2_360m_grpo_gsm8k_reasoner)
Code: https://github.com/Jaykef/ai-algorithms/blob/main/smollm2_360M_135M_grpo_gsm8k.ipynb
reacted to
DualityAI-RebekahBogdanoff's
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14 days ago
Post
3107
✨🎉Duality.ai just released a multiclass object detection dataset for YOLOv8, as well as a tutorial on how to create your own multiclass dataset!
Carefully crafted (not GenAI created) synthetic data that ACTUALLY trains a model that works in the physical world.
Create a free FalconEDU account, and download the 1000 image and annotation dataset - https://falcon.duality.ai/secure/documentation/ex3-dataset?sidebarMode=learn
-or-
Follow along with Exercise 3: Multiclass Object Detection to start creating - https://falcon.duality.ai/secure/documentation/ex3-objdetection-multiclass
-or-
Download this Colab notebook to see the data work, no hardware required - https://falcon.duality.ai/secure/documentation/ex3-dataset?sidebarMode=learn
Carefully crafted (not GenAI created) synthetic data that ACTUALLY trains a model that works in the physical world.
Create a free FalconEDU account, and download the 1000 image and annotation dataset - https://falcon.duality.ai/secure/documentation/ex3-dataset?sidebarMode=learn
-or-
Follow along with Exercise 3: Multiclass Object Detection to start creating - https://falcon.duality.ai/secure/documentation/ex3-objdetection-multiclass
-or-
Download this Colab notebook to see the data work, no hardware required - https://falcon.duality.ai/secure/documentation/ex3-dataset?sidebarMode=learn
reacted to
burtenshaw's
post with 🔥
16 days ago
Post
6204
Now the HF中国镜像站 agent course is getting real! With frameworks like smolagents, LlamaIndex, and LangChain.
🔗 Follow the org for updates https://huggingface.co/agents-course
This week we are releasing the first framework unit in the course and it’s on smolagents. This is what the unit covers:
- why should you use smolagents vs another library?
- how to build agents that use code
- build multiagents systems
- use vision language models for browser use
The team has been working flat out on this for a few weeks. Led by @sergiopaniego and supported by smolagents author @m-ric .
🔗 Follow the org for updates https://huggingface.co/agents-course
This week we are releasing the first framework unit in the course and it’s on smolagents. This is what the unit covers:
- why should you use smolagents vs another library?
- how to build agents that use code
- build multiagents systems
- use vision language models for browser use
The team has been working flat out on this for a few weeks. Led by @sergiopaniego and supported by smolagents author @m-ric .
reacted to
prithivMLmods's
post with 🚀
19 days ago
Post
5824
It's really interesting about the deployment of a new state of matter in Majorana 1: the world’s first quantum processor powered by topological qubits. If you missed this news this week, here are some links for you:
🅱️Topological qubit arrays: https://arxiv.org/pdf/2502.12252
⚛️ Quantum Blog: https://azure.microsoft.com/en-us/blog/quantum/2025/02/19/microsoft-unveils-majorana-1-the-worlds-first-quantum-processor-powered-by-topological-qubits/
📖 Read the story: https://news.microsoft.com/source/features/innovation/microsofts-majorana-1-chip-carves-new-path-for-quantum-computing/
📝 Majorana 1 Intro: https://youtu.be/Q4xCR20Dh1E?si=Z51DbEYnZFp_88Xp
🌀The Path to a Million Qubits: https://youtu.be/wSHmygPQukQ?si=TS80EhI62oWiMSHK
🅱️Topological qubit arrays: https://arxiv.org/pdf/2502.12252
⚛️ Quantum Blog: https://azure.microsoft.com/en-us/blog/quantum/2025/02/19/microsoft-unveils-majorana-1-the-worlds-first-quantum-processor-powered-by-topological-qubits/
📖 Read the story: https://news.microsoft.com/source/features/innovation/microsofts-majorana-1-chip-carves-new-path-for-quantum-computing/
📝 Majorana 1 Intro: https://youtu.be/Q4xCR20Dh1E?si=Z51DbEYnZFp_88Xp
🌀The Path to a Million Qubits: https://youtu.be/wSHmygPQukQ?si=TS80EhI62oWiMSHK
- 3 replies
reacted to
mmhamdy's
post with 🔥
21 days ago
Post
2733
🎉 We're excited to introduce MemoryCode, a novel synthetic dataset designed to rigorously evaluate LLMs' ability to track and execute coding instructions across multiple sessions. MemoryCode simulates realistic workplace scenarios where a mentee (the LLM) receives coding instructions from a mentor amidst a stream of both relevant and irrelevant information.
💡 But what makes MemoryCode unique?! The combination of the following:
✅ Multi-Session Dialogue Histories: MemoryCode consists of chronological sequences of dialogues between a mentor and a mentee, mirroring real-world interactions between coworkers.
✅ Interspersed Irrelevant Information: Critical instructions are deliberately interspersed with unrelated content, replicating the information overload common in office environments.
✅ Instruction Updates: Coding rules and conventions can be updated multiple times throughout the dialogue history, requiring LLMs to track and apply the most recent information.
✅ Prospective Memory: Unlike previous datasets that cue information retrieval, MemoryCode requires LLMs to spontaneously recall and apply relevant instructions without explicit prompts.
✅ Practical Task Execution: LLMs are evaluated on their ability to use the retrieved information to perform practical coding tasks, bridging the gap between information recall and real-world application.
📌 Our Findings
1️⃣ While even small models can handle isolated coding instructions, the performance of top-tier models like GPT-4o dramatically deteriorates when instructions are spread across multiple sessions.
2️⃣ This performance drop isn't simply due to the length of the context. Our analysis indicates that LLMs struggle to reason compositionally over sequences of instructions and updates. They have difficulty keeping track of which instructions are current and how to apply them.
🔗 Paper: From Tools to Teammates: Evaluating LLMs in Multi-Session Coding Interactions (2502.13791)
📦 Code: https://github.com/for-ai/MemoryCode
💡 But what makes MemoryCode unique?! The combination of the following:
✅ Multi-Session Dialogue Histories: MemoryCode consists of chronological sequences of dialogues between a mentor and a mentee, mirroring real-world interactions between coworkers.
✅ Interspersed Irrelevant Information: Critical instructions are deliberately interspersed with unrelated content, replicating the information overload common in office environments.
✅ Instruction Updates: Coding rules and conventions can be updated multiple times throughout the dialogue history, requiring LLMs to track and apply the most recent information.
✅ Prospective Memory: Unlike previous datasets that cue information retrieval, MemoryCode requires LLMs to spontaneously recall and apply relevant instructions without explicit prompts.
✅ Practical Task Execution: LLMs are evaluated on their ability to use the retrieved information to perform practical coding tasks, bridging the gap between information recall and real-world application.
📌 Our Findings
1️⃣ While even small models can handle isolated coding instructions, the performance of top-tier models like GPT-4o dramatically deteriorates when instructions are spread across multiple sessions.
2️⃣ This performance drop isn't simply due to the length of the context. Our analysis indicates that LLMs struggle to reason compositionally over sequences of instructions and updates. They have difficulty keeping track of which instructions are current and how to apply them.
🔗 Paper: From Tools to Teammates: Evaluating LLMs in Multi-Session Coding Interactions (2502.13791)
📦 Code: https://github.com/for-ai/MemoryCode
reacted to
lysandre's
post with ❤️
21 days ago
Post
5616
SmolVLM-2 and SigLIP-2 are now part of
They're added on top of the v4.49.0 release, and can be installed from the following tags:
This marks a new beginning for the release process of transformers. For the past five years, we've been doing monthly releases featuring many models (v4.49.0, the latest release, features 9 new architectures).
Starting with SmolVLM-2 & SigLIP2, we'll now additionally release tags supporting new models on a stable branch. These models are therefore directly available for use by installing from the tag itself. These tags will continue to be updated with fixes applied to these models.
Going forward, continue expecting software releases following semantic versioning: v4.50.0 will have ~10 new architectures compared to v4.49.0, as well as a myriad of new features, improvements and bug fixes. Accompanying these software releases, we'll release tags offering brand new models as fast as possible, to make them accessible to all immediately.
transformers in dedicated releases!They're added on top of the v4.49.0 release, and can be installed from the following tags:
v4.49.0-SmolVLM-2 and v4.49.0-SigLIP-2.This marks a new beginning for the release process of transformers. For the past five years, we've been doing monthly releases featuring many models (v4.49.0, the latest release, features 9 new architectures).
Starting with SmolVLM-2 & SigLIP2, we'll now additionally release tags supporting new models on a stable branch. These models are therefore directly available for use by installing from the tag itself. These tags will continue to be updated with fixes applied to these models.
Going forward, continue expecting software releases following semantic versioning: v4.50.0 will have ~10 new architectures compared to v4.49.0, as well as a myriad of new features, improvements and bug fixes. Accompanying these software releases, we'll release tags offering brand new models as fast as possible, to make them accessible to all immediately.
- 1 reply
reacted to
their
post with 🚀
22 days ago
Post
3577
🔥 Meet Muse: that can generate a game environment based on visuals or players’ controller actions. It was developed by Microsoft Research in collaboration with Ninja Theory (Hellblade developer). It’s built on something called the World and Human Action Model (WHAM-1.6B model). They trained on 7 years of Bleeding Edge gameplay and it can generate 2 minute long 3D game sequences with consistent physics and character behaviors all from just a second of input. They’ve gone and open-sourced it too. Open weights, the WHAM Demonstrator, and sample data on Azure AI Foundry for anyone to play with. Hope so soon on HF中国镜像站 🤗.
📄 Paper: https://www.nature.com/articles/s41586-025-08600-3
Blog Post: https://www.microsoft.com/en-us/research/blog/introducing-muse-our-first-generative-ai-model-designed-for-gameplay-ideation/
📄 Paper: https://www.nature.com/articles/s41586-025-08600-3
Blog Post: https://www.microsoft.com/en-us/research/blog/introducing-muse-our-first-generative-ai-model-designed-for-gameplay-ideation/
-
- 1 reply
replied to
their
post
22 days ago
It’s been added to HF中国镜像站 🤗.
Model: https://huggingface.co/microsoft/wham
Sample Dataset: https://huggingface.co/datasets/microsoft/bleeding-edge-gameplay-sample
reacted to
merve's
post with 🚀
22 days ago
Post
5900
Google just released PaliGemma 2 Mix: new versatile instruction vision language models 🔥
> Three new models: 3B, 10B, 28B with res 224, 448 💙
> Can do vision language tasks with open-ended prompts, understand documents, and segment or detect anything 🤯
Read more https://huggingface.co/blog/paligemma2mix
Try the demo google/paligemma2-10b-mix
All models are here google/paligemma-2-mix-67ac6a251aaf3ee73679dcc4
> Three new models: 3B, 10B, 28B with res 224, 448 💙
> Can do vision language tasks with open-ended prompts, understand documents, and segment or detect anything 🤯
Read more https://huggingface.co/blog/paligemma2mix
Try the demo google/paligemma2-10b-mix
All models are here google/paligemma-2-mix-67ac6a251aaf3ee73679dcc4
reacted to
burtenshaw's
post with 🚀
23 days ago
Post
7299
AGENTS + FINETUNING! This week HF中国镜像站 learn has a whole pathway on finetuning for agentic applications. You can follow these two courses to get knowledge on levelling up your agent game beyond prompts:
1️⃣ New Supervised Fine-tuning unit in the NLP Course https://huggingface.co/learn/nlp-course/en/chapter11/1
2️⃣New Finetuning for agents bonus module in the Agents Course https://huggingface.co/learn/agents-course/bonus-unit1/introduction
Fine-tuning will squeeze everything out of your model for how you’re using it, more than any prompt.
1️⃣ New Supervised Fine-tuning unit in the NLP Course https://huggingface.co/learn/nlp-course/en/chapter11/1
2️⃣New Finetuning for agents bonus module in the Agents Course https://huggingface.co/learn/agents-course/bonus-unit1/introduction
Fine-tuning will squeeze everything out of your model for how you’re using it, more than any prompt.
- 2 replies
posted
an
update
23 days ago
Post
3577
🔥 Meet Muse: that can generate a game environment based on visuals or players’ controller actions. It was developed by Microsoft Research in collaboration with Ninja Theory (Hellblade developer). It’s built on something called the World and Human Action Model (WHAM-1.6B model). They trained on 7 years of Bleeding Edge gameplay and it can generate 2 minute long 3D game sequences with consistent physics and character behaviors all from just a second of input. They’ve gone and open-sourced it too. Open weights, the WHAM Demonstrator, and sample data on Azure AI Foundry for anyone to play with. Hope so soon on HF中国镜像站 🤗.
📄 Paper: https://www.nature.com/articles/s41586-025-08600-3
Blog Post: https://www.microsoft.com/en-us/research/blog/introducing-muse-our-first-generative-ai-model-designed-for-gameplay-ideation/
📄 Paper: https://www.nature.com/articles/s41586-025-08600-3
Blog Post: https://www.microsoft.com/en-us/research/blog/introducing-muse-our-first-generative-ai-model-designed-for-gameplay-ideation/
-
- 1 reply
reacted to
fdaudens's
post with ❤️
23 days ago
Post
5806
🎯 Perplexity drops their FIRST open-weight model on HF中国镜像站: A decensored DeepSeek-R1 with full reasoning capabilities. Tested on 1000+ examples for unbiased responses.
Check it out: perplexity-ai/r1-1776
Blog post: https://perplexity.ai/hub/blog/open-sourcing-r1-1776
Check it out: perplexity-ai/r1-1776
Blog post: https://perplexity.ai/hub/blog/open-sourcing-r1-1776
- 1 reply
reacted to
clem's
post with ❤️
25 days ago
Post
3483
We crossed 1B+ tokens routed to inference providers partners on HF, that we released just a few days ago.
Just getting started of course but early users seem to like it & always happy to be able to partner with cool startups in the ecosystem.
Have you been using any integration and how can we make it better?
https://huggingface.co/blog/inference-providers
Just getting started of course but early users seem to like it & always happy to be able to partner with cool startups in the ecosystem.
Have you been using any integration and how can we make it better?
https://huggingface.co/blog/inference-providers
reacted to
jasoncorkill's
post with ❤️
30 days ago
Post
4547
Runway Gen-3 Alpha: The Style and Coherence Champion
Runway's latest video generation model, Gen-3 Alpha, is something special. It ranks #3 overall on our text-to-video human preference benchmark, but in terms of style and coherence, it outperforms even OpenAI Sora.
However, it struggles with alignment, making it less predictable for controlled outputs.
We've released a new dataset with human evaluations of Runway Gen-3 Alpha: Rapidata's text-2-video human preferences dataset. If you're working on video generation and want to see how your model compares to the biggest players, we can benchmark it for you.
🚀 DM us if you’re interested!
Dataset: Rapidata/text-2-video-human-preferences-runway-alpha
Runway's latest video generation model, Gen-3 Alpha, is something special. It ranks #3 overall on our text-to-video human preference benchmark, but in terms of style and coherence, it outperforms even OpenAI Sora.
However, it struggles with alignment, making it less predictable for controlled outputs.
We've released a new dataset with human evaluations of Runway Gen-3 Alpha: Rapidata's text-2-video human preferences dataset. If you're working on video generation and want to see how your model compares to the biggest players, we can benchmark it for you.
🚀 DM us if you’re interested!
Dataset: Rapidata/text-2-video-human-preferences-runway-alpha
- 1 reply
reacted to
ginipick's
post with 🔥
about 1 month ago
Post
5247
🌟 3D Llama Studio - AI 3D Generation Platform
📝 Project Overview
3D Llama Studio is an all-in-one AI platform that generates high-quality 3D models and stylized images from text or image inputs.
✨ Key Features
Text/Image to 3D Conversion 🎯
Generate 3D models from detailed text descriptions or reference images
Intuitive user interface
Text to Styled Image Generation 🎨
Customizable image generation settings
Adjustable resolution, generation steps, and guidance scale
Supports both English and Korean prompts
🛠️ Technical Features
Gradio-based web interface
Dark theme UI/UX
Real-time image generation and 3D modeling
💫 Highlights
User-friendly interface
Real-time preview
Random seed generation
High-resolution output support (up to 2048x2048)
🎯 Applications
Product design
Game asset creation
Architectural visualization
Educational 3D content
🔗 Try It Now!
Experience 3D Llama Studio:
ginigen/3D-LLAMA
#AI #3DGeneration #MachineLearning #ComputerVision #DeepLearning
📝 Project Overview
3D Llama Studio is an all-in-one AI platform that generates high-quality 3D models and stylized images from text or image inputs.
✨ Key Features
Text/Image to 3D Conversion 🎯
Generate 3D models from detailed text descriptions or reference images
Intuitive user interface
Text to Styled Image Generation 🎨
Customizable image generation settings
Adjustable resolution, generation steps, and guidance scale
Supports both English and Korean prompts
🛠️ Technical Features
Gradio-based web interface
Dark theme UI/UX
Real-time image generation and 3D modeling
💫 Highlights
User-friendly interface
Real-time preview
Random seed generation
High-resolution output support (up to 2048x2048)
🎯 Applications
Product design
Game asset creation
Architectural visualization
Educational 3D content
🔗 Try It Now!
Experience 3D Llama Studio:
ginigen/3D-LLAMA
#AI #3DGeneration #MachineLearning #ComputerVision #DeepLearning